{"id":5559,"date":"2023-09-04T03:08:16","date_gmt":"2023-09-04T03:08:16","guid":{"rendered":"https:\/\/cm.vastapps.dev\/tcia-collection\/sln-breast\/"},"modified":"2023-09-13T11:57:13","modified_gmt":"2023-09-13T11:57:13","slug":"sln-breast","status":"publish","type":"tcia_collection","link":"https:\/\/cm.vastapps.dev\/tcia-collection\/sln-breast\/","title":{"rendered":"SLN-BREAST"},"featured_media":0,"template":"","citation-tax":[],"cancer_types":["Breast Cancer"],"citations":[4400,4401,2925],"collection_doi":"10.7937\/tcia.2019.3xbn2jcc","collection_download_info":"\nClick the Versions tab for more info about data releases.","collection_downloads":[4946,4947],"full_export":"

Summary<\/h2>

<\/p>The detection of breast cancer metastases to lymph nodes is of great prognostic value for patient treatment. Using machine learning to detect metastatic breast cancer to lymph nodes can increase efficiency of pathologist diagnosis and ultimately ensure patients are accurately staged for prospective treatment. This dataset allows for the objective comparison of breast cancer metastases detection algorithms.<\/p>

The dataset consists of 130 de-identified whole slide images of H&E stained axillary lymph node specimens from 78 patients. Metastatic breast carcinoma is present in 36 of the WSI from 27 patients. No patient inclusion\/exclusion criteria were followed. No slide inclusion\/exclusion criteria were followed. The slides were scanned at Memorial Sloan Kettering Cancer Center (MSKCC) with Leica Aperio AT2 scanners at 20x equivalent magnification (0.5 microns per pixel). Together with the slides, the class label of each slide, either positive or negative for breast carcinoma, is given. The slide class label was obtained from the pathology report of the respective case.

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